Virtual Cooperation for Throughput Maximization in Distributed Large-Scale Wireless Networks
Bibliographic record
Abstract
A distributed wireless network with links is considered, where the links are partitioned into clusters each operating in a subchannel with bandwidth . The subchannels are assumed to be orthogonal to each other. A general shadow-fading model described by the probability of shadowing and the average cross-link gains is considered. The main goal is to find the maximum network throughput in the asymptotic regime of , which is achieved by: (i) proposing a distributed power allocation strategy, where the objective of each user is to maximize its best estimate (based on its local information) of the average network throughput and (ii) choosing the optimum value for . In the first part, the network throughput is defined as the average sum-rate of the network, which is shown to scale as . It is proved that the optimum power allocation strategy for each user for large is a threshold-based on-off scheme. In the second part, the network throughput is defined as the guaranteed sum-rate, when the outage probability approaches zero. It is demonstrated that the on-off power scheme maximizes the throughput, which scales as . Moreover, the optimum spectrum sharing for maximizing the average sum-rate and the guaranteed sum-rate is achieved at .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".